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        <h1>Perturbation Biology</h1>
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            <span class="header-emphasis">Data</span>-driven
            <span class="header-emphasis">Models</span> for
            <span class="header-emphasis">Prediction</span> of
            <span class="header-emphasis">Response</span> to Combinatorial
            <span class="header-emphasis">Perturbation</span>
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        <h4>What is <span class="stressed-text">perturbation biology</span>?</h4>
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            Perturbation biology is an experimental-computational technology for inferring
            network models that predict the response of cells to perturbations,
            and that may be useful in the design of combinatorial therapy against cancer.
            Beyond nomination of effective drug combinations, the perturbation biology
            method paves the way for model-driven quantitative cell biology with diverse
            applications in many fields of biology.
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        <img src="images/figure1_bpmel.png" class="img-responsive" alt="BP mel">
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        <h4>How does <span class="stressed-text">perturbation biology</span> work?</h4>
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            Perturbation biology involves systematic perturbations of cells with combinations of
            targeted compounds (Box 1-2), high-throughput measurements of response profiles (Box 2),
            automated extraction of prior signaling information from databases (Box 3-4),
            construction of ODE-based signaling pathway models (Box 5) with the belief propagation (BP)
            based network inference algorithm (Box 6) and prediction of system response to
            novel perturbations with the models and simulations (Box 7).
            The "prior extraction and reduction algorithm" (PERA) generates a qualitative prior model,
            which is a network of known interactions between the proteins of interest
            (i.e., profiled (phospho)proteins). This is achieved through a search in
            the Pathway Commons information resource, which integrates biological pathway information
            from multiple public databases (Box 3-4). In the quantitative network models,
            the nodes represent measured levels of (phospho)proteins or cellular phenotypes and
            the edges represent the influence of the upstream nodes on the time derivative of
            their downstream effectors. This definition corresponds to a simple yet efficient
            ODE-based mathematical description of models (Box 5). Our BP-based modeling approach
            combines information from the perturbation data (phosphoproteomic and phenotypic) with
            prior information to generate network models of signaling (Box 6). We execute the resulting
            ODE based models to predict system response to untested perturbation conditions (Box 7).
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